深度学习 · graph neural networks论文趋势 2023–2026

主要结论

• 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
• NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是数据集与基准：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 20 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks是 0.4 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

全部深度学习 · graph neural networks论文

共 1,648 篇，第 15 / 33 页

701. ICML 2024Generalization Error of Graph Neural Networks in the Mean-field Regime
702. ICML 2024Generalized Sobolev Transport for Probability Measures on a Graph
703. ICML 2024Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
704. ICML 2024Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
705. ICML 2024Graph Adversarial Diffusion Convolution
706. ICML 2024Graph As Point Set
707. ICML 2024Graph Automorphism Group Equivariant Neural Networks
708. ICML 2024Graph Distillation with Eigenbasis Matching
709. ICML 2024Graph External Attention Enhanced Transformer
710. ICML 2024Graph Geometry-Preserving Autoencoders
711. ICML 2024Graph Mixup on Approximate Gromov–Wasserstein Geodesics
712. ICML 2024Graph Neural Network Explanations are Fragile
713. ICML 2024Graph Neural Networks Use Graphs When They Shouldn't
714. ICML 2024Graph Neural Networks with a Distribution of Parametrized Graphs
715. ICML 2024Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
716. ICML 2024Graph Out-of-Distribution Detection Goes Neighborhood Shaping
717. ICML 2024Graph Structure Extrapolation for Out-of-Distribution Generalization
718. ICML 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
719. ICML 2024Graph-Triggered Rising Bandits
720. ICML 2024HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
721. ICML 2024HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
722. ICML 2024Homomorphism Counts for Graph Neural Networks: All About That Basis
723. ICML 2024How Graph Neural Networks Learn: Lessons from Training Dynamics
724. ICML 2024How Interpretable Are Interpretable Graph Neural Networks?
725. ICML 2024Hypergraph-enhanced Dual Semi-supervised Graph Classification
726. ICML 2024Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
727. ICML 2024Individual Fairness in Graph Decomposition
728. ICML 2024Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
729. ICML 2024Learning Divergence Fields for Shift-Robust Graph Representations
730. ICML 2024Learning Graph Representation via Graph Entropy Maximization
731. ICML 2024Less is More: on the Over-Globalizing Problem in Graph Transformers
732. ICML 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
733. ICML 2024Modelling Microbial Communities with Graph Neural Networks
734. ICML 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
735. ICML 2024Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
736. ICML 2024On the Expressive Power of Spectral Invariant Graph Neural Networks
737. ICML 2024On the Generalization of Equivariant Graph Neural Networks
738. ICML 2024On the Role of Edge Dependency in Graph Generative Models
739. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
740. ICML 2024Perfect Alignment May be Poisonous to Graph Contrastive Learning
741. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
742. ICML 2024Position: Graph Foundation Models Are Already Here
743. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
744. ICML 2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
745. ICML 2024Quantum Positional Encodings for Graph Neural Networks
746. ICML 2024Recurrent Distance Filtering for Graph Representation Learning
747. ICML 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
748. ICML 2024Robust Graph Matching when Nodes are Corrupt
749. ICML 2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
750. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？

共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？

NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。

ICLR 2026 有多少篇深度学习 · graph neural networks论文？

ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。

ICML 2026 有多少篇深度学习 · graph neural networks论文？

ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。

CVPR 2026 有多少篇深度学习 · graph neural networks论文？

CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。

哪个会议的深度学习 · graph neural networks论文最多？

NeurIPS，2023–2026 年共 842 篇。